I cut a client's AI inference costs from $3,200/month to $180/month.

Same performance. Same latency. Here's exactly what I did.

Step 1: Replaced GPT-4 calls with a fine-tuned Llama 7B for their specific use case. The task was structured extraction — overkill for a frontier model.

Step 2: Added aggressive caching. 40% of their queries were near-duplicates. A simple semantic cache with 0.95 similarity threshold eliminated redundant API calls.

Step 3: Batched requests. Instead of one-at-a-time processing, grouped requests and processed them together. This alone reduced per-request overhead by 60%.

Step 4: Quantized the model from fp16 to int4. Minimal quality drop, 75% memory reduction, which meant we could use a cheaper GPU instance.

Most AI cost problems aren't about the model being expensive. They're about using an expensive model when a cheap one works, recomputing things you've already computed, and processing inefficiently.

Before scaling up your GPU budget, audit your usage patterns. The savings are usually hiding in plain sight.